Development of a Scalable Monitoring System for Wheelchair Tilt-in-Space Usage

Tim D. Yang, Seth Andrew Hutchinson, Laura A Rice, Kenneth Lloyd Watkin, Yih-Kuen Jan

Research output: Contribution to journalArticlepeer-review


Despite advances in rehabilitation research and assistive technology, pressure ulcer rates remain high among people affected by spinal cord injury. The Rehabilitation Engineering and Assistive Technology Society of North America recommends using power seat functions, such as tilt-in-space (tilt), to reduce seating interface pressure and restore blood flow to ischemic tissues. However, recent studies suggest that pressure-relieving tilts are not being adequately used in daily life. Additional data collection is needed to better understand tilt trends in daily life, but the conventional data logging procedures that involve the physical initialization, retrieval, and management of the data loggers may not be practical in a large-scale setting. Thus, we developed a longitudinal monitoring framework with a focus on scalability through connectivity and affordability. Connectivity was achieved through wireless technologies, allowing the data to be both recorded and retrieved remotely, along with Web technologies, allowing the server-processed results to be viewed in real time from any internet-accessible browser. Affordability was achieved through the use of free software and relatively inexpensive components, including the Raspberry Pi and MMA7455L accelerometer. Preliminary validation was performed by comparing the tilt distributions of our automated online processing system with a manual offsite processing method. Two protocols were tested, and statistical analysis revealed a correlation coefficient of 1.00 in both cases. Thus, our system may be a viable means of both broadening and deepening the scope of longitudinal monitoring by reaching more participants and analyzing more data over longer periods.
Original languageEnglish (US)
JournalInternational Journal of Physical Medicine & Rehabilitation
Issue number04
StatePublished - 2013


  • Accelerometer
  • Raspberry Pi
  • Spinal cord injury
  • Python
  • Pressure ulcers
  • Longitudinal monitoring
  • Django


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